Overview

Brought to you by YData

Dataset statistics

Number of variables45
Number of observations101766
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory189.3 MiB
Average record size in memory1.9 KiB

Variable types

Categorical32
Numeric11
Text1
Boolean1

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
change is highly overall correlated with diabetesMed and 1 other fieldsHigh correlation
diabetesMed is highly overall correlated with change and 1 other fieldsHigh correlation
insulin is highly overall correlated with change and 1 other fieldsHigh correlation
race is highly imbalanced (57.2%)Imbalance
weight is highly imbalanced (79.9%)Imbalance
max_glu_serum is highly imbalanced (81.2%)Imbalance
A1Cresult is highly imbalanced (54.8%)Imbalance
metformin is highly imbalanced (59.5%)Imbalance
repaglinide is highly imbalanced (93.9%)Imbalance
nateglinide is highly imbalanced (96.9%)Imbalance
chlorpropamide is highly imbalanced (99.5%)Imbalance
glimepiride is highly imbalanced (84.0%)Imbalance
acetohexamide is highly imbalanced (> 99.9%)Imbalance
glipizide is highly imbalanced (69.2%)Imbalance
glyburide is highly imbalanced (72.3%)Imbalance
tolbutamide is highly imbalanced (99.7%)Imbalance
pioglitazone is highly imbalanced (80.2%)Imbalance
rosiglitazone is highly imbalanced (82.2%)Imbalance
acarbose is highly imbalanced (98.5%)Imbalance
miglitol is highly imbalanced (99.7%)Imbalance
troglitazone is highly imbalanced (> 99.9%)Imbalance
tolazamide is highly imbalanced (99.7%)Imbalance
glyburide-metformin is highly imbalanced (97.0%)Imbalance
glipizide-metformin is highly imbalanced (99.8%)Imbalance
glimepiride-pioglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-rosiglitazone is highly imbalanced (> 99.9%)Imbalance
metformin-pioglitazone is highly imbalanced (> 99.9%)Imbalance
number_emergency is highly skewed (γ1 = 22.85558215)Skewed
num_procedures has 46652 (45.8%) zerosZeros
number_outpatient has 85027 (83.6%) zerosZeros
number_emergency has 90383 (88.8%) zerosZeros
number_inpatient has 67630 (66.5%) zerosZeros

Reproduction

Analysis started2026-01-02 18:42:45.350349
Analysis finished2026-01-02 18:42:58.935410
Duration13.59 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

race
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.7 MiB
Caucasian
78372 
AfricanAmerican
19210 
Hispanic
 
2037
Other
 
1506
Asian
 
641

Length

Max length15
Median length9
Mean length10.028192
Min length5

Characters and Unicode

Total characters1020529
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCaucasian
2nd rowCaucasian
3rd rowAfricanAmerican
4th rowCaucasian
5th rowCaucasian

Common Values

ValueCountFrequency (%)
Caucasian78372
77.0%
AfricanAmerican19210
 
18.9%
Hispanic2037
 
2.0%
Other1506
 
1.5%
Asian641
 
0.6%

Length

2026-01-02T11:42:58.956451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:58.977603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
caucasian78372
77.0%
africanamerican19210
 
18.9%
hispanic2037
 
2.0%
other1506
 
1.5%
asian641
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a276214
27.1%
i121507
11.9%
n119470
11.7%
c118829
11.6%
s81050
 
7.9%
C78372
 
7.7%
u78372
 
7.7%
r39926
 
3.9%
A39061
 
3.8%
e20716
 
2.0%
Other values (7)47012
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1020529
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a276214
27.1%
i121507
11.9%
n119470
11.7%
c118829
11.6%
s81050
 
7.9%
C78372
 
7.7%
u78372
 
7.7%
r39926
 
3.9%
A39061
 
3.8%
e20716
 
2.0%
Other values (7)47012
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1020529
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a276214
27.1%
i121507
11.9%
n119470
11.7%
c118829
11.6%
s81050
 
7.9%
C78372
 
7.7%
u78372
 
7.7%
r39926
 
3.9%
A39061
 
3.8%
e20716
 
2.0%
Other values (7)47012
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1020529
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a276214
27.1%
i121507
11.9%
n119470
11.7%
c118829
11.6%
s81050
 
7.9%
C78372
 
7.7%
u78372
 
7.7%
r39926
 
3.9%
A39061
 
3.8%
e20716
 
2.0%
Other values (7)47012
 
4.6%

gender
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.2 MiB
Female
54708 
Male
47055 
Unknown/Invalid
 
3

Length

Max length15
Median length6
Mean length5.0754967
Min length4

Characters and Unicode

Total characters516513
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female54708
53.8%
Male47055
46.2%
Unknown/Invalid3
 
< 0.1%

Length

2026-01-02T11:42:59.003208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.020481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female54708
53.8%
male47055
46.2%
unknown/invalid3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e156471
30.3%
a101766
19.7%
l101766
19.7%
F54708
 
10.6%
m54708
 
10.6%
M47055
 
9.1%
n12
 
< 0.1%
U3
 
< 0.1%
k3
 
< 0.1%
o3
 
< 0.1%
Other values (6)18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)516513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e156471
30.3%
a101766
19.7%
l101766
19.7%
F54708
 
10.6%
m54708
 
10.6%
M47055
 
9.1%
n12
 
< 0.1%
U3
 
< 0.1%
k3
 
< 0.1%
o3
 
< 0.1%
Other values (6)18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)516513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e156471
30.3%
a101766
19.7%
l101766
19.7%
F54708
 
10.6%
m54708
 
10.6%
M47055
 
9.1%
n12
 
< 0.1%
U3
 
< 0.1%
k3
 
< 0.1%
o3
 
< 0.1%
Other values (6)18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)516513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e156471
30.3%
a101766
19.7%
l101766
19.7%
F54708
 
10.6%
m54708
 
10.6%
M47055
 
9.1%
n12
 
< 0.1%
U3
 
< 0.1%
k3
 
< 0.1%
o3
 
< 0.1%
Other values (6)18
 
< 0.1%

age
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.4 MiB
[70-80)
26068 
[60-70)
22483 
[50-60)
17256 
[80-90)
17197 
[40-50)
9685 
Other values (5)
9077 

Length

Max length8
Median length7
Mean length7.0258633
Min length6

Characters and Unicode

Total characters714994
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0-10)
2nd row[10-20)
3rd row[20-30)
4th row[30-40)
5th row[40-50)

Common Values

ValueCountFrequency (%)
[70-80)26068
25.6%
[60-70)22483
22.1%
[50-60)17256
17.0%
[80-90)17197
16.9%
[40-50)9685
 
9.5%
[30-40)3775
 
3.7%
[90-100)2793
 
2.7%
[20-30)1657
 
1.6%
[10-20)691
 
0.7%
[0-10)161
 
0.2%

Length

2026-01-02T11:42:59.044745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.070296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
70-8026068
25.6%
60-7022483
22.1%
50-6017256
17.0%
80-9017197
16.9%
40-509685
 
9.5%
30-403775
 
3.7%
90-1002793
 
2.7%
20-301657
 
1.6%
10-20691
 
0.7%
0-10161
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0206325
28.9%
[101766
14.2%
-101766
14.2%
)101766
14.2%
748551
 
6.8%
843265
 
6.1%
639739
 
5.6%
526941
 
3.8%
919990
 
2.8%
413460
 
1.9%
Other values (3)11425
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)714994
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0206325
28.9%
[101766
14.2%
-101766
14.2%
)101766
14.2%
748551
 
6.8%
843265
 
6.1%
639739
 
5.6%
526941
 
3.8%
919990
 
2.8%
413460
 
1.9%
Other values (3)11425
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)714994
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0206325
28.9%
[101766
14.2%
-101766
14.2%
)101766
14.2%
748551
 
6.8%
843265
 
6.1%
639739
 
5.6%
526941
 
3.8%
919990
 
2.8%
413460
 
1.9%
Other values (3)11425
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)714994
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0206325
28.9%
[101766
14.2%
-101766
14.2%
)101766
14.2%
748551
 
6.8%
843265
 
6.1%
639739
 
5.6%
526941
 
3.8%
919990
 
2.8%
413460
 
1.9%
Other values (3)11425
 
1.6%

weight
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
0
98569 
1
 
3197

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters101766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

Length

2026-01-02T11:42:59.102136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.116309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

Most occurring characters

ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)101766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)101766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)101766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
098569
96.9%
13197
 
3.1%

admission_type_id
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240061
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.130228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4454028
Coefficient of variation (CV)0.7141297
Kurtosis1.9424761
Mean2.0240061
Median Absolute Deviation (MAD)0
Skewness1.5919843
Sum205975
Variance2.0891893
MonotonicityNot monotonic
2026-01-02T11:42:59.149633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
153990
53.1%
318869
 
18.5%
218480
 
18.2%
65291
 
5.2%
54785
 
4.7%
8320
 
0.3%
721
 
< 0.1%
410
 
< 0.1%
ValueCountFrequency (%)
153990
53.1%
218480
 
18.2%
318869
 
18.5%
410
 
< 0.1%
54785
 
4.7%
65291
 
5.2%
721
 
< 0.1%
8320
 
0.3%
ValueCountFrequency (%)
8320
 
0.3%
721
 
< 0.1%
65291
 
5.2%
54785
 
4.7%
410
 
< 0.1%
318869
 
18.5%
218480
 
18.2%
153990
53.1%

discharge_disposition_id
Real number (ℝ)

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7156418
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.172626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile18
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.2801655
Coefficient of variation (CV)1.4210642
Kurtosis6.0033468
Mean3.7156418
Median Absolute Deviation (MAD)0
Skewness2.563067
Sum378126
Variance27.880148
MonotonicityNot monotonic
2026-01-02T11:42:59.197151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
160234
59.2%
313954
 
13.7%
612902
 
12.7%
183691
 
3.6%
22128
 
2.1%
221993
 
2.0%
111642
 
1.6%
51184
 
1.2%
25989
 
1.0%
4815
 
0.8%
Other values (16)2234
 
2.2%
ValueCountFrequency (%)
160234
59.2%
22128
 
2.1%
313954
 
13.7%
4815
 
0.8%
51184
 
1.2%
612902
 
12.7%
7623
 
0.6%
8108
 
0.1%
921
 
< 0.1%
106
 
< 0.1%
ValueCountFrequency (%)
28139
 
0.1%
275
 
< 0.1%
25989
 
1.0%
2448
 
< 0.1%
23412
 
0.4%
221993
2.0%
202
 
< 0.1%
198
 
< 0.1%
183691
3.6%
1714
 
< 0.1%

admission_source_id
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7544366
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.219842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q37
95-th percentile17
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0640808
Coefficient of variation (CV)0.70625173
Kurtosis1.7449894
Mean5.7544366
Median Absolute Deviation (MAD)0
Skewness1.0299349
Sum585606
Variance16.516753
MonotonicityNot monotonic
2026-01-02T11:42:59.242154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
757494
56.5%
129565
29.1%
176781
 
6.7%
43187
 
3.1%
62264
 
2.2%
21104
 
1.1%
5855
 
0.8%
3187
 
0.2%
20161
 
0.2%
9125
 
0.1%
Other values (7)43
 
< 0.1%
ValueCountFrequency (%)
129565
29.1%
21104
 
1.1%
3187
 
0.2%
43187
 
3.1%
5855
 
0.8%
62264
 
2.2%
757494
56.5%
816
 
< 0.1%
9125
 
0.1%
108
 
< 0.1%
ValueCountFrequency (%)
252
 
< 0.1%
2212
 
< 0.1%
20161
 
0.2%
176781
6.7%
142
 
< 0.1%
131
 
< 0.1%
112
 
< 0.1%
108
 
< 0.1%
9125
 
0.1%
816
 
< 0.1%

time_in_hospital
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3959869
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.263226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile11
Maximum14
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9851078
Coefficient of variation (CV)0.67905293
Kurtosis0.85025084
Mean4.3959869
Median Absolute Deviation (MAD)2
Skewness1.1339987
Sum447362
Variance8.9108684
MonotonicityNot monotonic
2026-01-02T11:42:59.286128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
317756
17.4%
217224
16.9%
114208
14.0%
413924
13.7%
59966
9.8%
67539
7.4%
75859
 
5.8%
84391
 
4.3%
93002
 
2.9%
102342
 
2.3%
Other values (4)5555
 
5.5%
ValueCountFrequency (%)
114208
14.0%
217224
16.9%
317756
17.4%
413924
13.7%
59966
9.8%
67539
7.4%
75859
 
5.8%
84391
 
4.3%
93002
 
2.9%
102342
 
2.3%
ValueCountFrequency (%)
141042
 
1.0%
131210
 
1.2%
121448
 
1.4%
111855
 
1.8%
102342
 
2.3%
93002
 
2.9%
84391
4.3%
75859
5.8%
67539
7.4%
59966
9.8%
Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
2026-01-02T11:42:59.322863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length36
Median length33
Mean length11.557603
Min length6

Characters and Unicode

Total characters1176171
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowPediatrics-Endocrinology
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown49949
49.1%
internalmedicine14635
 
14.4%
emergency/trauma7565
 
7.4%
family/generalpractice7440
 
7.3%
cardiology5352
 
5.3%
surgery-general3099
 
3.0%
nephrology1613
 
1.6%
orthopedics1400
 
1.4%
orthopedics-reconstructive1233
 
1.2%
radiologist1140
 
1.1%
Other values (63)8340
 
8.2%
2026-01-02T11:42:59.393242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n218645
18.6%
e105151
 
8.9%
o84002
 
7.1%
r76899
 
6.5%
a71149
 
6.0%
i63308
 
5.4%
U50634
 
4.3%
c50007
 
4.3%
k49950
 
4.2%
w49950
 
4.2%
Other values (33)356476
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1176171
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n218645
18.6%
e105151
 
8.9%
o84002
 
7.1%
r76899
 
6.5%
a71149
 
6.0%
i63308
 
5.4%
U50634
 
4.3%
c50007
 
4.3%
k49950
 
4.2%
w49950
 
4.2%
Other values (33)356476
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1176171
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n218645
18.6%
e105151
 
8.9%
o84002
 
7.1%
r76899
 
6.5%
a71149
 
6.0%
i63308
 
5.4%
U50634
 
4.3%
c50007
 
4.3%
k49950
 
4.2%
w49950
 
4.2%
Other values (33)356476
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1176171
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n218645
18.6%
e105151
 
8.9%
o84002
 
7.1%
r76899
 
6.5%
a71149
 
6.0%
i63308
 
5.4%
U50634
 
4.3%
c50007
 
4.3%
k49950
 
4.2%
w49950
 
4.2%
Other values (33)356476
30.3%

num_lab_procedures
Real number (ℝ)

Distinct118
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.095641
Minimum1
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.426097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q131
median44
Q357
95-th percentile73
Maximum132
Range131
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.674362
Coefficient of variation (CV)0.45652789
Kurtosis-0.24507352
Mean43.095641
Median Absolute Deviation (MAD)13
Skewness-0.23654392
Sum4385671
Variance387.08053
MonotonicityNot monotonic
2026-01-02T11:42:59.457290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13208
 
3.2%
432804
 
2.8%
442496
 
2.5%
452376
 
2.3%
382213
 
2.2%
402201
 
2.2%
462189
 
2.2%
412117
 
2.1%
422113
 
2.1%
472106
 
2.1%
Other values (108)77943
76.6%
ValueCountFrequency (%)
13208
3.2%
21101
 
1.1%
3668
 
0.7%
4378
 
0.4%
5286
 
0.3%
6282
 
0.3%
7323
 
0.3%
8366
 
0.4%
9933
 
0.9%
10838
 
0.8%
ValueCountFrequency (%)
1321
 
< 0.1%
1291
 
< 0.1%
1261
 
< 0.1%
1211
 
< 0.1%
1201
 
< 0.1%
1181
 
< 0.1%
1142
< 0.1%
1133
< 0.1%
1113
< 0.1%
1094
< 0.1%

num_procedures
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3397304
Minimum0
Maximum6
Zeros46652
Zeros (%)45.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.479400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.705807
Coefficient of variation (CV)1.2732465
Kurtosis0.8571103
Mean1.3397304
Median Absolute Deviation (MAD)1
Skewness1.3164148
Sum136339
Variance2.9097775
MonotonicityNot monotonic
2026-01-02T11:42:59.497808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
046652
45.8%
120742
20.4%
212717
 
12.5%
39443
 
9.3%
64954
 
4.9%
44180
 
4.1%
53078
 
3.0%
ValueCountFrequency (%)
046652
45.8%
120742
20.4%
212717
 
12.5%
39443
 
9.3%
44180
 
4.1%
53078
 
3.0%
64954
 
4.9%
ValueCountFrequency (%)
64954
 
4.9%
53078
 
3.0%
44180
 
4.1%
39443
 
9.3%
212717
 
12.5%
120742
20.4%
046652
45.8%

num_medications
Real number (ℝ)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021844
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.522854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q110
median15
Q320
95-th percentile31
Maximum81
Range80
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.1275662
Coefficient of variation (CV)0.50728032
Kurtosis3.4681549
Mean16.021844
Median Absolute Deviation (MAD)5
Skewness1.3266721
Sum1630479
Variance66.057332
MonotonicityNot monotonic
2026-01-02T11:42:59.555304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
136086
 
6.0%
126004
 
5.9%
115795
 
5.7%
155792
 
5.7%
145707
 
5.6%
165430
 
5.3%
105346
 
5.3%
174919
 
4.8%
94913
 
4.8%
184523
 
4.4%
Other values (65)47251
46.4%
ValueCountFrequency (%)
1262
 
0.3%
2470
 
0.5%
3900
 
0.9%
41417
 
1.4%
52017
 
2.0%
62699
2.7%
73484
3.4%
84353
4.3%
94913
4.8%
105346
5.3%
ValueCountFrequency (%)
811
 
< 0.1%
791
 
< 0.1%
752
 
< 0.1%
741
 
< 0.1%
723
< 0.1%
702
 
< 0.1%
695
< 0.1%
687
< 0.1%
677
< 0.1%
665
< 0.1%

number_outpatient
Real number (ℝ)

Zeros 

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36935715
Minimum0
Maximum42
Zeros85027
Zeros (%)83.6%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.585436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2672651
Coefficient of variation (CV)3.4310019
Kurtosis147.90774
Mean0.36935715
Median Absolute Deviation (MAD)0
Skewness8.8329589
Sum37588
Variance1.6059608
MonotonicityNot monotonic
2026-01-02T11:42:59.611388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
085027
83.6%
18547
 
8.4%
23594
 
3.5%
32042
 
2.0%
41099
 
1.1%
5533
 
0.5%
6303
 
0.3%
7155
 
0.2%
898
 
0.1%
983
 
0.1%
Other values (29)285
 
0.3%
ValueCountFrequency (%)
085027
83.6%
18547
 
8.4%
23594
 
3.5%
32042
 
2.0%
41099
 
1.1%
5533
 
0.5%
6303
 
0.3%
7155
 
0.2%
898
 
0.1%
983
 
0.1%
ValueCountFrequency (%)
421
< 0.1%
401
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%
362
< 0.1%
352
< 0.1%
341
< 0.1%
332
< 0.1%
292
< 0.1%

number_emergency
Real number (ℝ)

Skewed  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19783621
Minimum0
Maximum76
Zeros90383
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.635930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.93047227
Coefficient of variation (CV)4.7032455
Kurtosis1191.6867
Mean0.19783621
Median Absolute Deviation (MAD)0
Skewness22.855582
Sum20133
Variance0.86577864
MonotonicityNot monotonic
2026-01-02T11:42:59.662223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
090383
88.8%
17677
 
7.5%
22042
 
2.0%
3725
 
0.7%
4374
 
0.4%
5192
 
0.2%
694
 
0.1%
773
 
0.1%
850
 
< 0.1%
1034
 
< 0.1%
Other values (23)122
 
0.1%
ValueCountFrequency (%)
090383
88.8%
17677
 
7.5%
22042
 
2.0%
3725
 
0.7%
4374
 
0.4%
5192
 
0.2%
694
 
0.1%
773
 
0.1%
850
 
< 0.1%
933
 
< 0.1%
ValueCountFrequency (%)
761
< 0.1%
641
< 0.1%
631
< 0.1%
541
< 0.1%
461
< 0.1%
421
< 0.1%
371
< 0.1%
291
< 0.1%
281
< 0.1%
252
< 0.1%

number_inpatient
Real number (ℝ)

Zeros 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.63556591
Minimum0
Maximum21
Zeros67630
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.686123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum21
Range21
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2628633
Coefficient of variation (CV)1.9869903
Kurtosis20.719397
Mean0.63556591
Median Absolute Deviation (MAD)0
Skewness3.614139
Sum64679
Variance1.5948237
MonotonicityNot monotonic
2026-01-02T11:42:59.709327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
067630
66.5%
119521
 
19.2%
27566
 
7.4%
33411
 
3.4%
41622
 
1.6%
5812
 
0.8%
6480
 
0.5%
7268
 
0.3%
8151
 
0.1%
9111
 
0.1%
Other values (11)194
 
0.2%
ValueCountFrequency (%)
067630
66.5%
119521
 
19.2%
27566
 
7.4%
33411
 
3.4%
41622
 
1.6%
5812
 
0.8%
6480
 
0.5%
7268
 
0.3%
8151
 
0.1%
9111
 
0.1%
ValueCountFrequency (%)
211
 
< 0.1%
192
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
166
 
< 0.1%
159
 
< 0.1%
1410
 
< 0.1%
1320
< 0.1%
1234
< 0.1%
1149
< 0.1%

number_diagnoses
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4226068
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size795.2 KiB
2026-01-02T11:42:59.730203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q39
95-th percentile9
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9336001
Coefficient of variation (CV)0.26050149
Kurtosis-0.079056024
Mean7.4226068
Median Absolute Deviation (MAD)1
Skewness-0.87674624
Sum755369
Variance3.7388095
MonotonicityNot monotonic
2026-01-02T11:42:59.751799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
949474
48.6%
511393
 
11.2%
810616
 
10.4%
710393
 
10.2%
610161
 
10.0%
45537
 
5.4%
32835
 
2.8%
21023
 
1.0%
1219
 
0.2%
1645
 
< 0.1%
Other values (6)70
 
0.1%
ValueCountFrequency (%)
1219
 
0.2%
21023
 
1.0%
32835
 
2.8%
45537
 
5.4%
511393
 
11.2%
610161
 
10.0%
710393
 
10.2%
810616
 
10.4%
949474
48.6%
1017
 
< 0.1%
ValueCountFrequency (%)
1645
 
< 0.1%
1510
 
< 0.1%
147
 
< 0.1%
1316
 
< 0.1%
129
 
< 0.1%
1111
 
< 0.1%
1017
 
< 0.1%
949474
48.6%
810616
 
10.4%
710393
 
10.2%

max_glu_serum
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
NotPerformed
96420 
Norm
 
2597
>200
 
1485
>300
 
1264

Length

Max length12
Median length12
Mean length11.579742
Min length4

Characters and Unicode

Total characters1178424
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNotPerformed
2nd rowNotPerformed
3rd rowNotPerformed
4th rowNotPerformed
5th rowNotPerformed

Common Values

ValueCountFrequency (%)
NotPerformed96420
94.7%
Norm2597
 
2.6%
>2001485
 
1.5%
>3001264
 
1.2%

Length

2026-01-02T11:42:59.777349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.796100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
notperformed96420
94.7%
norm2597
 
2.6%
2001485
 
1.5%
3001264
 
1.2%

Most occurring characters

ValueCountFrequency (%)
o195437
16.6%
r195437
16.6%
e192840
16.4%
N99017
8.4%
m99017
8.4%
t96420
8.2%
P96420
8.2%
f96420
8.2%
d96420
8.2%
05498
 
0.5%
Other values (3)5498
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1178424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o195437
16.6%
r195437
16.6%
e192840
16.4%
N99017
8.4%
m99017
8.4%
t96420
8.2%
P96420
8.2%
f96420
8.2%
d96420
8.2%
05498
 
0.5%
Other values (3)5498
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1178424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o195437
16.6%
r195437
16.6%
e192840
16.4%
N99017
8.4%
m99017
8.4%
t96420
8.2%
P96420
8.2%
f96420
8.2%
d96420
8.2%
05498
 
0.5%
Other values (3)5498
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1178424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o195437
16.6%
r195437
16.6%
e192840
16.4%
N99017
8.4%
m99017
8.4%
t96420
8.2%
P96420
8.2%
f96420
8.2%
d96420
8.2%
05498
 
0.5%
Other values (3)5498
 
0.5%

A1Cresult
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.8 MiB
NotPerformed
84748 
>8
 
8216
Norm
 
4990
>7
 
3812

Length

Max length12
Median length12
Mean length10.4258
Min length2

Characters and Unicode

Total characters1060992
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNotPerformed
2nd rowNotPerformed
3rd rowNotPerformed
4th rowNotPerformed
5th rowNotPerformed

Common Values

ValueCountFrequency (%)
NotPerformed84748
83.3%
>88216
 
8.1%
Norm4990
 
4.9%
>73812
 
3.7%

Length

2026-01-02T11:42:59.816756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.833044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
notperformed84748
83.3%
88216
 
8.1%
norm4990
 
4.9%
73812
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o174486
16.4%
r174486
16.4%
e169496
16.0%
N89738
8.5%
m89738
8.5%
t84748
8.0%
P84748
8.0%
f84748
8.0%
d84748
8.0%
>12028
 
1.1%
Other values (2)12028
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1060992
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o174486
16.4%
r174486
16.4%
e169496
16.0%
N89738
8.5%
m89738
8.5%
t84748
8.0%
P84748
8.0%
f84748
8.0%
d84748
8.0%
>12028
 
1.1%
Other values (2)12028
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1060992
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o174486
16.4%
r174486
16.4%
e169496
16.0%
N89738
8.5%
m89738
8.5%
t84748
8.0%
P84748
8.0%
f84748
8.0%
d84748
8.0%
>12028
 
1.1%
Other values (2)12028
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1060992
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o174486
16.4%
r174486
16.4%
e169496
16.0%
N89738
8.5%
m89738
8.5%
t84748
8.0%
P84748
8.0%
f84748
8.0%
d84748
8.0%
>12028
 
1.1%
Other values (2)12028
 
1.1%

metformin
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
81778 
Steady
18346 
Up
 
1067
Down
 
575

Length

Max length6
Median length2
Mean length2.7324057
Min length2

Characters and Unicode

Total characters278066
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No81778
80.4%
Steady18346
 
18.0%
Up1067
 
1.0%
Down575
 
0.6%

Length

2026-01-02T11:42:59.856591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.874812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no81778
80.4%
steady18346
 
18.0%
up1067
 
1.0%
down575
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o82353
29.6%
N81778
29.4%
S18346
 
6.6%
t18346
 
6.6%
e18346
 
6.6%
a18346
 
6.6%
d18346
 
6.6%
y18346
 
6.6%
U1067
 
0.4%
p1067
 
0.4%
Other values (3)1725
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)278066
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o82353
29.6%
N81778
29.4%
S18346
 
6.6%
t18346
 
6.6%
e18346
 
6.6%
a18346
 
6.6%
d18346
 
6.6%
y18346
 
6.6%
U1067
 
0.4%
p1067
 
0.4%
Other values (3)1725
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)278066
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o82353
29.6%
N81778
29.4%
S18346
 
6.6%
t18346
 
6.6%
e18346
 
6.6%
a18346
 
6.6%
d18346
 
6.6%
y18346
 
6.6%
U1067
 
0.4%
p1067
 
0.4%
Other values (3)1725
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)278066
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o82353
29.6%
N81778
29.4%
S18346
 
6.6%
t18346
 
6.6%
e18346
 
6.6%
a18346
 
6.6%
d18346
 
6.6%
y18346
 
6.6%
U1067
 
0.4%
p1067
 
0.4%
Other values (3)1725
 
0.6%

repaglinide
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
100227 
Steady
 
1384
Up
 
110
Down
 
45

Length

Max length6
Median length2
Mean length2.0552837
Min length2

Characters and Unicode

Total characters209158
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No100227
98.5%
Steady1384
 
1.4%
Up110
 
0.1%
Down45
 
< 0.1%

Length

2026-01-02T11:42:59.898813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.917028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no100227
98.5%
steady1384
 
1.4%
up110
 
0.1%
down45
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o100272
47.9%
N100227
47.9%
S1384
 
0.7%
t1384
 
0.7%
e1384
 
0.7%
a1384
 
0.7%
d1384
 
0.7%
y1384
 
0.7%
U110
 
0.1%
p110
 
0.1%
Other values (3)135
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)209158
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o100272
47.9%
N100227
47.9%
S1384
 
0.7%
t1384
 
0.7%
e1384
 
0.7%
a1384
 
0.7%
d1384
 
0.7%
y1384
 
0.7%
U110
 
0.1%
p110
 
0.1%
Other values (3)135
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)209158
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o100272
47.9%
N100227
47.9%
S1384
 
0.7%
t1384
 
0.7%
e1384
 
0.7%
a1384
 
0.7%
d1384
 
0.7%
y1384
 
0.7%
U110
 
0.1%
p110
 
0.1%
Other values (3)135
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)209158
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o100272
47.9%
N100227
47.9%
S1384
 
0.7%
t1384
 
0.7%
e1384
 
0.7%
a1384
 
0.7%
d1384
 
0.7%
y1384
 
0.7%
U110
 
0.1%
p110
 
0.1%
Other values (3)135
 
0.1%

nateglinide
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
101063 
Steady
 
668
Up
 
24
Down
 
11

Length

Max length6
Median length2
Mean length2.0264725
Min length2

Characters and Unicode

Total characters206226
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101063
99.3%
Steady668
 
0.7%
Up24
 
< 0.1%
Down11
 
< 0.1%

Length

2026-01-02T11:42:59.940369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:42:59.958851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101063
99.3%
steady668
 
0.7%
up24
 
< 0.1%
down11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o101074
49.0%
N101063
49.0%
S668
 
0.3%
t668
 
0.3%
e668
 
0.3%
a668
 
0.3%
d668
 
0.3%
y668
 
0.3%
U24
 
< 0.1%
p24
 
< 0.1%
Other values (3)33
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)206226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o101074
49.0%
N101063
49.0%
S668
 
0.3%
t668
 
0.3%
e668
 
0.3%
a668
 
0.3%
d668
 
0.3%
y668
 
0.3%
U24
 
< 0.1%
p24
 
< 0.1%
Other values (3)33
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)206226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o101074
49.0%
N101063
49.0%
S668
 
0.3%
t668
 
0.3%
e668
 
0.3%
a668
 
0.3%
d668
 
0.3%
y668
 
0.3%
U24
 
< 0.1%
p24
 
< 0.1%
Other values (3)33
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)206226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o101074
49.0%
N101063
49.0%
S668
 
0.3%
t668
 
0.3%
e668
 
0.3%
a668
 
0.3%
d668
 
0.3%
y668
 
0.3%
U24
 
< 0.1%
p24
 
< 0.1%
Other values (3)33
 
< 0.1%

chlorpropamide
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
101680 
Steady
 
79
Up
 
6
Down
 
1

Length

Max length6
Median length2
Mean length2.0031248
Min length2

Characters and Unicode

Total characters203850
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101680
99.9%
Steady79
 
0.1%
Up6
 
< 0.1%
Down1
 
< 0.1%

Length

2026-01-02T11:43:00.039400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.058192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101680
99.9%
steady79
 
0.1%
up6
 
< 0.1%
down1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o101681
49.9%
N101680
49.9%
S79
 
< 0.1%
t79
 
< 0.1%
e79
 
< 0.1%
a79
 
< 0.1%
d79
 
< 0.1%
y79
 
< 0.1%
U6
 
< 0.1%
p6
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o101681
49.9%
N101680
49.9%
S79
 
< 0.1%
t79
 
< 0.1%
e79
 
< 0.1%
a79
 
< 0.1%
d79
 
< 0.1%
y79
 
< 0.1%
U6
 
< 0.1%
p6
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o101681
49.9%
N101680
49.9%
S79
 
< 0.1%
t79
 
< 0.1%
e79
 
< 0.1%
a79
 
< 0.1%
d79
 
< 0.1%
y79
 
< 0.1%
U6
 
< 0.1%
p6
 
< 0.1%
Other values (3)3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o101681
49.9%
N101680
49.9%
S79
 
< 0.1%
t79
 
< 0.1%
e79
 
< 0.1%
a79
 
< 0.1%
d79
 
< 0.1%
y79
 
< 0.1%
U6
 
< 0.1%
p6
 
< 0.1%
Other values (3)3
 
< 0.1%

glimepiride
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
96575 
Steady
 
4670
Up
 
327
Down
 
194

Length

Max length6
Median length2
Mean length2.187371
Min length2

Characters and Unicode

Total characters222600
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No96575
94.9%
Steady4670
 
4.6%
Up327
 
0.3%
Down194
 
0.2%

Length

2026-01-02T11:43:00.080941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.099028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no96575
94.9%
steady4670
 
4.6%
up327
 
0.3%
down194
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o96769
43.5%
N96575
43.4%
S4670
 
2.1%
t4670
 
2.1%
e4670
 
2.1%
a4670
 
2.1%
d4670
 
2.1%
y4670
 
2.1%
U327
 
0.1%
p327
 
0.1%
Other values (3)582
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)222600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o96769
43.5%
N96575
43.4%
S4670
 
2.1%
t4670
 
2.1%
e4670
 
2.1%
a4670
 
2.1%
d4670
 
2.1%
y4670
 
2.1%
U327
 
0.1%
p327
 
0.1%
Other values (3)582
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)222600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o96769
43.5%
N96575
43.4%
S4670
 
2.1%
t4670
 
2.1%
e4670
 
2.1%
a4670
 
2.1%
d4670
 
2.1%
y4670
 
2.1%
U327
 
0.1%
p327
 
0.1%
Other values (3)582
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)222600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o96769
43.5%
N96575
43.4%
S4670
 
2.1%
t4670
 
2.1%
e4670
 
2.1%
a4670
 
2.1%
d4670
 
2.1%
y4670
 
2.1%
U327
 
0.1%
p327
 
0.1%
Other values (3)582
 
0.3%

acetohexamide
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2026-01-02T11:43:00.123067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.139609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

glipizide
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
89080 
Steady
11356 
Up
 
770
Down
 
560

Length

Max length6
Median length2
Mean length2.457363
Min length2

Characters and Unicode

Total characters250076
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowSteady
4th rowNo
5th rowSteady

Common Values

ValueCountFrequency (%)
No89080
87.5%
Steady11356
 
11.2%
Up770
 
0.8%
Down560
 
0.6%

Length

2026-01-02T11:43:00.159878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.178375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no89080
87.5%
steady11356
 
11.2%
up770
 
0.8%
down560
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o89640
35.8%
N89080
35.6%
S11356
 
4.5%
t11356
 
4.5%
e11356
 
4.5%
a11356
 
4.5%
d11356
 
4.5%
y11356
 
4.5%
U770
 
0.3%
p770
 
0.3%
Other values (3)1680
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)250076
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o89640
35.8%
N89080
35.6%
S11356
 
4.5%
t11356
 
4.5%
e11356
 
4.5%
a11356
 
4.5%
d11356
 
4.5%
y11356
 
4.5%
U770
 
0.3%
p770
 
0.3%
Other values (3)1680
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)250076
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o89640
35.8%
N89080
35.6%
S11356
 
4.5%
t11356
 
4.5%
e11356
 
4.5%
a11356
 
4.5%
d11356
 
4.5%
y11356
 
4.5%
U770
 
0.3%
p770
 
0.3%
Other values (3)1680
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)250076
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o89640
35.8%
N89080
35.6%
S11356
 
4.5%
t11356
 
4.5%
e11356
 
4.5%
a11356
 
4.5%
d11356
 
4.5%
y11356
 
4.5%
U770
 
0.3%
p770
 
0.3%
Other values (3)1680
 
0.7%

glyburide
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
91116 
Steady
9274 
Up
 
812
Down
 
564

Length

Max length6
Median length2
Mean length2.3756068
Min length2

Characters and Unicode

Total characters241756
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No91116
89.5%
Steady9274
 
9.1%
Up812
 
0.8%
Down564
 
0.6%

Length

2026-01-02T11:43:00.202566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.221028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no91116
89.5%
steady9274
 
9.1%
up812
 
0.8%
down564
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o91680
37.9%
N91116
37.7%
S9274
 
3.8%
t9274
 
3.8%
e9274
 
3.8%
a9274
 
3.8%
d9274
 
3.8%
y9274
 
3.8%
U812
 
0.3%
p812
 
0.3%
Other values (3)1692
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)241756
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o91680
37.9%
N91116
37.7%
S9274
 
3.8%
t9274
 
3.8%
e9274
 
3.8%
a9274
 
3.8%
d9274
 
3.8%
y9274
 
3.8%
U812
 
0.3%
p812
 
0.3%
Other values (3)1692
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)241756
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o91680
37.9%
N91116
37.7%
S9274
 
3.8%
t9274
 
3.8%
e9274
 
3.8%
a9274
 
3.8%
d9274
 
3.8%
y9274
 
3.8%
U812
 
0.3%
p812
 
0.3%
Other values (3)1692
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)241756
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o91680
37.9%
N91116
37.7%
S9274
 
3.8%
t9274
 
3.8%
e9274
 
3.8%
a9274
 
3.8%
d9274
 
3.8%
y9274
 
3.8%
U812
 
0.3%
p812
 
0.3%
Other values (3)1692
 
0.7%

tolbutamide
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101743 
Steady
 
23

Length

Max length6
Median length2
Mean length2.000904
Min length2

Characters and Unicode

Total characters203624
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101743
> 99.9%
Steady23
 
< 0.1%

Length

2026-01-02T11:43:00.244975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.261743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101743
> 99.9%
steady23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101743
50.0%
o101743
50.0%
S23
 
< 0.1%
t23
 
< 0.1%
e23
 
< 0.1%
a23
 
< 0.1%
d23
 
< 0.1%
y23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101743
50.0%
o101743
50.0%
S23
 
< 0.1%
t23
 
< 0.1%
e23
 
< 0.1%
a23
 
< 0.1%
d23
 
< 0.1%
y23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101743
50.0%
o101743
50.0%
S23
 
< 0.1%
t23
 
< 0.1%
e23
 
< 0.1%
a23
 
< 0.1%
d23
 
< 0.1%
y23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101743
50.0%
o101743
50.0%
S23
 
< 0.1%
t23
 
< 0.1%
e23
 
< 0.1%
a23
 
< 0.1%
d23
 
< 0.1%
y23
 
< 0.1%

pioglitazone
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
94438 
Steady
 
6976
Up
 
234
Down
 
118

Length

Max length6
Median length2
Mean length2.2765167
Min length2

Characters and Unicode

Total characters231672
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No94438
92.8%
Steady6976
 
6.9%
Up234
 
0.2%
Down118
 
0.1%

Length

2026-01-02T11:43:00.281662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.299794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no94438
92.8%
steady6976
 
6.9%
up234
 
0.2%
down118
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o94556
40.8%
N94438
40.8%
S6976
 
3.0%
t6976
 
3.0%
e6976
 
3.0%
a6976
 
3.0%
d6976
 
3.0%
y6976
 
3.0%
U234
 
0.1%
p234
 
0.1%
Other values (3)354
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)231672
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o94556
40.8%
N94438
40.8%
S6976
 
3.0%
t6976
 
3.0%
e6976
 
3.0%
a6976
 
3.0%
d6976
 
3.0%
y6976
 
3.0%
U234
 
0.1%
p234
 
0.1%
Other values (3)354
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)231672
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o94556
40.8%
N94438
40.8%
S6976
 
3.0%
t6976
 
3.0%
e6976
 
3.0%
a6976
 
3.0%
d6976
 
3.0%
y6976
 
3.0%
U234
 
0.1%
p234
 
0.1%
Other values (3)354
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)231672
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o94556
40.8%
N94438
40.8%
S6976
 
3.0%
t6976
 
3.0%
e6976
 
3.0%
a6976
 
3.0%
d6976
 
3.0%
y6976
 
3.0%
U234
 
0.1%
p234
 
0.1%
Other values (3)354
 
0.2%

rosiglitazone
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
95401 
Steady
 
6100
Up
 
178
Down
 
87

Length

Max length6
Median length2
Mean length2.2414755
Min length2

Characters and Unicode

Total characters228106
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No95401
93.7%
Steady6100
 
6.0%
Up178
 
0.2%
Down87
 
0.1%

Length

2026-01-02T11:43:00.322971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.341242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no95401
93.7%
steady6100
 
6.0%
up178
 
0.2%
down87
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o95488
41.9%
N95401
41.8%
S6100
 
2.7%
t6100
 
2.7%
e6100
 
2.7%
a6100
 
2.7%
d6100
 
2.7%
y6100
 
2.7%
U178
 
0.1%
p178
 
0.1%
Other values (3)261
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)228106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o95488
41.9%
N95401
41.8%
S6100
 
2.7%
t6100
 
2.7%
e6100
 
2.7%
a6100
 
2.7%
d6100
 
2.7%
y6100
 
2.7%
U178
 
0.1%
p178
 
0.1%
Other values (3)261
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)228106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o95488
41.9%
N95401
41.8%
S6100
 
2.7%
t6100
 
2.7%
e6100
 
2.7%
a6100
 
2.7%
d6100
 
2.7%
y6100
 
2.7%
U178
 
0.1%
p178
 
0.1%
Other values (3)261
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)228106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o95488
41.9%
N95401
41.8%
S6100
 
2.7%
t6100
 
2.7%
e6100
 
2.7%
a6100
 
2.7%
d6100
 
2.7%
y6100
 
2.7%
U178
 
0.1%
p178
 
0.1%
Other values (3)261
 
0.1%

acarbose
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
101458 
Steady
 
295
Up
 
10
Down
 
3

Length

Max length6
Median length2
Mean length2.0116542
Min length2

Characters and Unicode

Total characters204718
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101458
99.7%
Steady295
 
0.3%
Up10
 
< 0.1%
Down3
 
< 0.1%

Length

2026-01-02T11:43:00.364259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.382515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101458
99.7%
steady295
 
0.3%
up10
 
< 0.1%
down3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o101461
49.6%
N101458
49.6%
S295
 
0.1%
t295
 
0.1%
e295
 
0.1%
a295
 
0.1%
d295
 
0.1%
y295
 
0.1%
U10
 
< 0.1%
p10
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)204718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o101461
49.6%
N101458
49.6%
S295
 
0.1%
t295
 
0.1%
e295
 
0.1%
a295
 
0.1%
d295
 
0.1%
y295
 
0.1%
U10
 
< 0.1%
p10
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)204718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o101461
49.6%
N101458
49.6%
S295
 
0.1%
t295
 
0.1%
e295
 
0.1%
a295
 
0.1%
d295
 
0.1%
y295
 
0.1%
U10
 
< 0.1%
p10
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)204718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o101461
49.6%
N101458
49.6%
S295
 
0.1%
t295
 
0.1%
e295
 
0.1%
a295
 
0.1%
d295
 
0.1%
y295
 
0.1%
U10
 
< 0.1%
p10
 
< 0.1%
Other values (3)9
 
< 0.1%

miglitol
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101728 
Steady
 
31
Down
 
5
Up
 
2

Length

Max length6
Median length2
Mean length2.0013167
Min length2

Characters and Unicode

Total characters203666
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101728
> 99.9%
Steady31
 
< 0.1%
Down5
 
< 0.1%
Up2
 
< 0.1%

Length

2026-01-02T11:43:00.405803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.423835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101728
> 99.9%
steady31
 
< 0.1%
down5
 
< 0.1%
up2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o101733
50.0%
N101728
49.9%
S31
 
< 0.1%
t31
 
< 0.1%
e31
 
< 0.1%
a31
 
< 0.1%
d31
 
< 0.1%
y31
 
< 0.1%
D5
 
< 0.1%
w5
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o101733
50.0%
N101728
49.9%
S31
 
< 0.1%
t31
 
< 0.1%
e31
 
< 0.1%
a31
 
< 0.1%
d31
 
< 0.1%
y31
 
< 0.1%
D5
 
< 0.1%
w5
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o101733
50.0%
N101728
49.9%
S31
 
< 0.1%
t31
 
< 0.1%
e31
 
< 0.1%
a31
 
< 0.1%
d31
 
< 0.1%
y31
 
< 0.1%
D5
 
< 0.1%
w5
 
< 0.1%
Other values (3)9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o101733
50.0%
N101728
49.9%
S31
 
< 0.1%
t31
 
< 0.1%
e31
 
< 0.1%
a31
 
< 0.1%
d31
 
< 0.1%
y31
 
< 0.1%
D5
 
< 0.1%
w5
 
< 0.1%
Other values (3)9
 
< 0.1%

troglitazone
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101763 
Steady
 
3

Length

Max length6
Median length2
Mean length2.0001179
Min length2

Characters and Unicode

Total characters203544
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101763
> 99.9%
Steady3
 
< 0.1%

Length

2026-01-02T11:43:00.446512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.462225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101763
> 99.9%
steady3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101763
50.0%
o101763
50.0%
S3
 
< 0.1%
t3
 
< 0.1%
e3
 
< 0.1%
a3
 
< 0.1%
d3
 
< 0.1%
y3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203544
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101763
50.0%
o101763
50.0%
S3
 
< 0.1%
t3
 
< 0.1%
e3
 
< 0.1%
a3
 
< 0.1%
d3
 
< 0.1%
y3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203544
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101763
50.0%
o101763
50.0%
S3
 
< 0.1%
t3
 
< 0.1%
e3
 
< 0.1%
a3
 
< 0.1%
d3
 
< 0.1%
y3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203544
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101763
50.0%
o101763
50.0%
S3
 
< 0.1%
t3
 
< 0.1%
e3
 
< 0.1%
a3
 
< 0.1%
d3
 
< 0.1%
y3
 
< 0.1%

tolazamide
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101727 
Steady
 
38
Up
 
1

Length

Max length6
Median length2
Mean length2.0014936
Min length2

Characters and Unicode

Total characters203684
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101727
> 99.9%
Steady38
 
< 0.1%
Up1
 
< 0.1%

Length

2026-01-02T11:43:00.481728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.705702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101727
> 99.9%
steady38
 
< 0.1%
up1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101727
49.9%
o101727
49.9%
S38
 
< 0.1%
t38
 
< 0.1%
e38
 
< 0.1%
a38
 
< 0.1%
d38
 
< 0.1%
y38
 
< 0.1%
U1
 
< 0.1%
p1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101727
49.9%
o101727
49.9%
S38
 
< 0.1%
t38
 
< 0.1%
e38
 
< 0.1%
a38
 
< 0.1%
d38
 
< 0.1%
y38
 
< 0.1%
U1
 
< 0.1%
p1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101727
49.9%
o101727
49.9%
S38
 
< 0.1%
t38
 
< 0.1%
e38
 
< 0.1%
a38
 
< 0.1%
d38
 
< 0.1%
y38
 
< 0.1%
U1
 
< 0.1%
p1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101727
49.9%
o101727
49.9%
S38
 
< 0.1%
t38
 
< 0.1%
e38
 
< 0.1%
a38
 
< 0.1%
d38
 
< 0.1%
y38
 
< 0.1%
U1
 
< 0.1%
p1
 
< 0.1%

insulin
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.1 MiB
No
47383 
Steady
30849 
Down
12218 
Up
11316 

Length

Max length6
Median length2
Mean length3.4526659
Min length2

Characters and Unicode

Total characters351364
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowUp
3rd rowNo
4th rowUp
5th rowSteady

Common Values

ValueCountFrequency (%)
No47383
46.6%
Steady30849
30.3%
Down12218
 
12.0%
Up11316
 
11.1%

Length

2026-01-02T11:43:00.728496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.748134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no47383
46.6%
steady30849
30.3%
down12218
 
12.0%
up11316
 
11.1%

Most occurring characters

ValueCountFrequency (%)
o59601
17.0%
N47383
13.5%
S30849
8.8%
t30849
8.8%
e30849
8.8%
a30849
8.8%
d30849
8.8%
y30849
8.8%
D12218
 
3.5%
w12218
 
3.5%
Other values (3)34850
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)351364
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o59601
17.0%
N47383
13.5%
S30849
8.8%
t30849
8.8%
e30849
8.8%
a30849
8.8%
d30849
8.8%
y30849
8.8%
D12218
 
3.5%
w12218
 
3.5%
Other values (3)34850
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)351364
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o59601
17.0%
N47383
13.5%
S30849
8.8%
t30849
8.8%
e30849
8.8%
a30849
8.8%
d30849
8.8%
y30849
8.8%
D12218
 
3.5%
w12218
 
3.5%
Other values (3)34850
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)351364
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o59601
17.0%
N47383
13.5%
S30849
8.8%
t30849
8.8%
e30849
8.8%
a30849
8.8%
d30849
8.8%
y30849
8.8%
D12218
 
3.5%
w12218
 
3.5%
Other values (3)34850
9.9%

glyburide-metformin
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
No
101060 
Steady
 
692
Up
 
8
Down
 
6

Length

Max length6
Median length2
Mean length2.0273176
Min length2

Characters and Unicode

Total characters206312
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101060
99.3%
Steady692
 
0.7%
Up8
 
< 0.1%
Down6
 
< 0.1%

Length

2026-01-02T11:43:00.774962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.792827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101060
99.3%
steady692
 
0.7%
up8
 
< 0.1%
down6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o101066
49.0%
N101060
49.0%
S692
 
0.3%
t692
 
0.3%
e692
 
0.3%
a692
 
0.3%
d692
 
0.3%
y692
 
0.3%
U8
 
< 0.1%
p8
 
< 0.1%
Other values (3)18
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)206312
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o101066
49.0%
N101060
49.0%
S692
 
0.3%
t692
 
0.3%
e692
 
0.3%
a692
 
0.3%
d692
 
0.3%
y692
 
0.3%
U8
 
< 0.1%
p8
 
< 0.1%
Other values (3)18
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)206312
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o101066
49.0%
N101060
49.0%
S692
 
0.3%
t692
 
0.3%
e692
 
0.3%
a692
 
0.3%
d692
 
0.3%
y692
 
0.3%
U8
 
< 0.1%
p8
 
< 0.1%
Other values (3)18
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)206312
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o101066
49.0%
N101060
49.0%
S692
 
0.3%
t692
 
0.3%
e692
 
0.3%
a692
 
0.3%
d692
 
0.3%
y692
 
0.3%
U8
 
< 0.1%
p8
 
< 0.1%
Other values (3)18
 
< 0.1%

glipizide-metformin
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101753 
Steady
 
13

Length

Max length6
Median length2
Mean length2.000511
Min length2

Characters and Unicode

Total characters203584
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101753
> 99.9%
Steady13
 
< 0.1%

Length

2026-01-02T11:43:00.816598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.832899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101753
> 99.9%
steady13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101753
50.0%
o101753
50.0%
S13
 
< 0.1%
t13
 
< 0.1%
e13
 
< 0.1%
a13
 
< 0.1%
d13
 
< 0.1%
y13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101753
50.0%
o101753
50.0%
S13
 
< 0.1%
t13
 
< 0.1%
e13
 
< 0.1%
a13
 
< 0.1%
d13
 
< 0.1%
y13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101753
50.0%
o101753
50.0%
S13
 
< 0.1%
t13
 
< 0.1%
e13
 
< 0.1%
a13
 
< 0.1%
d13
 
< 0.1%
y13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101753
50.0%
o101753
50.0%
S13
 
< 0.1%
t13
 
< 0.1%
e13
 
< 0.1%
a13
 
< 0.1%
d13
 
< 0.1%
y13
 
< 0.1%

glimepiride-pioglitazone
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2026-01-02T11:43:00.853281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.869552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

metformin-rosiglitazone
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101764 
Steady
 
2

Length

Max length6
Median length2
Mean length2.0000786
Min length2

Characters and Unicode

Total characters203540
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101764
> 99.9%
Steady2
 
< 0.1%

Length

2026-01-02T11:43:00.889765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.906539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101764
> 99.9%
steady2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101764
50.0%
o101764
50.0%
S2
 
< 0.1%
t2
 
< 0.1%
e2
 
< 0.1%
a2
 
< 0.1%
d2
 
< 0.1%
y2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203540
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101764
50.0%
o101764
50.0%
S2
 
< 0.1%
t2
 
< 0.1%
e2
 
< 0.1%
a2
 
< 0.1%
d2
 
< 0.1%
y2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203540
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101764
50.0%
o101764
50.0%
S2
 
< 0.1%
t2
 
< 0.1%
e2
 
< 0.1%
a2
 
< 0.1%
d2
 
< 0.1%
y2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203540
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101764
50.0%
o101764
50.0%
S2
 
< 0.1%
t2
 
< 0.1%
e2
 
< 0.1%
a2
 
< 0.1%
d2
 
< 0.1%
y2
 
< 0.1%

metformin-pioglitazone
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
101765 
Steady
 
1

Length

Max length6
Median length2
Mean length2.0000393
Min length2

Characters and Unicode

Total characters203536
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No101765
> 99.9%
Steady1
 
< 0.1%

Length

2026-01-02T11:43:00.926431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.942878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no101765
> 99.9%
steady1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N101765
50.0%
o101765
50.0%
S1
 
< 0.1%
t1
 
< 0.1%
e1
 
< 0.1%
a1
 
< 0.1%
d1
 
< 0.1%
y1
 
< 0.1%

change
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.9 MiB
No
54755 
Ch
47011 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters203532
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowCh
3rd rowNo
4th rowCh
5th rowCh

Common Values

ValueCountFrequency (%)
No54755
53.8%
Ch47011
46.2%

Length

2026-01-02T11:43:00.960907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:00.975596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no54755
53.8%
ch47011
46.2%

Most occurring characters

ValueCountFrequency (%)
N54755
26.9%
o54755
26.9%
C47011
23.1%
h47011
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)203532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N54755
26.9%
o54755
26.9%
C47011
23.1%
h47011
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)203532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N54755
26.9%
o54755
26.9%
C47011
23.1%
h47011
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)203532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N54755
26.9%
o54755
26.9%
C47011
23.1%
h47011
23.1%

diabetesMed
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.5 KiB
True
78363 
False
23403 
ValueCountFrequency (%)
True78363
77.0%
False23403
 
23.0%
2026-01-02T11:43:00.987802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

readmitted
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 MiB
NO
54864 
>30
35545 
<30
11357 

Length

Max length3
Median length2
Mean length2.4608808
Min length2

Characters and Unicode

Total characters250434
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNO
2nd row>30
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO54864
53.9%
>3035545
34.9%
<3011357
 
11.2%

Length

2026-01-02T11:43:01.007014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-02T11:43:01.023663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no54864
53.9%
3046902
46.1%

Most occurring characters

ValueCountFrequency (%)
N54864
21.9%
O54864
21.9%
346902
18.7%
046902
18.7%
>35545
14.2%
<11357
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)250434
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N54864
21.9%
O54864
21.9%
346902
18.7%
046902
18.7%
>35545
14.2%
<11357
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)250434
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N54864
21.9%
O54864
21.9%
346902
18.7%
046902
18.7%
>35545
14.2%
<11357
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)250434
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N54864
21.9%
O54864
21.9%
346902
18.7%
046902
18.7%
>35545
14.2%
<11357
 
4.5%

diag_1_category
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Diseases Of The Circulatory System
30336 
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
11459 
Diseases Of The Respiratory System
10407 
Diseases Of The Digestive System
9208 
Symptoms, Signs, And Ill-Defined Conditions
7636 
Other values (14)
32720 

Length

Max length98
Median length71
Mean length39.288652
Min length7

Characters and Unicode

Total characters3998249
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
2nd rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
3rd rowComplications Of Pregnancy, Childbirth, And The Puerperium
4th rowInfectious And Parasitic Diseases
5th rowNeoplasms

Common Values

ValueCountFrequency (%)
Diseases Of The Circulatory System30336
29.8%
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders11459
 
11.3%
Diseases Of The Respiratory System10407
 
10.2%
Diseases Of The Digestive System9208
 
9.0%
Symptoms, Signs, And Ill-Defined Conditions7636
 
7.5%
Injury And Poisoning6974
 
6.9%
Diseases Of The Genitourinary System5078
 
5.0%
Diseases Of The Musculoskeletal System And Connective Tissue4957
 
4.9%
Neoplasms3433
 
3.4%
Infectious And Parasitic Diseases2768
 
2.7%
Other values (9)9510
 
9.3%

Length

2026-01-02T11:43:01.046999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diseases79057
14.5%
of67163
12.3%
the65517
12.0%
system61197
11.2%
and52429
 
9.6%
circulatory30336
 
5.6%
disorders13721
 
2.5%
endocrine11459
 
2.1%
nutritional11459
 
2.1%
metabolic11459
 
2.1%
Other values (42)140595
25.8%

Most occurring characters

ValueCountFrequency (%)
442626
 
11.1%
s428826
 
10.7%
e426256
 
10.7%
i301667
 
7.5%
t215444
 
5.4%
n196666
 
4.9%
a183461
 
4.6%
r164055
 
4.1%
o162578
 
4.1%
y135420
 
3.4%
Other values (35)1341250
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3998249
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
442626
 
11.1%
s428826
 
10.7%
e426256
 
10.7%
i301667
 
7.5%
t215444
 
5.4%
n196666
 
4.9%
a183461
 
4.6%
r164055
 
4.1%
o162578
 
4.1%
y135420
 
3.4%
Other values (35)1341250
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3998249
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
442626
 
11.1%
s428826
 
10.7%
e426256
 
10.7%
i301667
 
7.5%
t215444
 
5.4%
n196666
 
4.9%
a183461
 
4.6%
r164055
 
4.1%
o162578
 
4.1%
y135420
 
3.4%
Other values (35)1341250
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3998249
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
442626
 
11.1%
s428826
 
10.7%
e426256
 
10.7%
i301667
 
7.5%
t215444
 
5.4%
n196666
 
4.9%
a183461
 
4.6%
r164055
 
4.1%
o162578
 
4.1%
y135420
 
3.4%
Other values (35)1341250
33.5%

diag_2_category
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Diseases Of The Circulatory System
31365 
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
21017 
Diseases Of The Respiratory System
10251 
Diseases Of The Genitourinary System
7987 
Symptoms, Signs, And Ill-Defined Conditions
4632 
Other values (14)
26514 

Length

Max length98
Median length71
Mean length42.970697
Min length7

Characters and Unicode

Total characters4372956
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
3rd rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
4th rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
5th rowNeoplasms

Common Values

ValueCountFrequency (%)
Diseases Of The Circulatory System31365
30.8%
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders21017
20.7%
Diseases Of The Respiratory System10251
 
10.1%
Diseases Of The Genitourinary System7987
 
7.8%
Symptoms, Signs, And Ill-Defined Conditions4632
 
4.6%
Diseases Of The Digestive System3962
 
3.9%
Diseases Of The Skin And Subcutaneous Tissue3596
 
3.5%
Diseases Of The Blood And Blood-Forming Organs2926
 
2.9%
Mental Disorders2657
 
2.6%
Neoplasms2547
 
2.5%
Other values (9)10826
 
10.6%

Length

2026-01-02T11:43:01.072809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diseases86085
14.7%
of66819
11.4%
the63552
10.9%
and63548
10.9%
system56615
9.7%
circulatory31365
 
5.4%
disorders23674
 
4.1%
endocrine21017
 
3.6%
nutritional21017
 
3.6%
metabolic21017
 
3.6%
Other values (42)129053
22.1%

Most occurring characters

ValueCountFrequency (%)
481996
 
11.0%
s439542
 
10.1%
e437496
 
10.0%
i339957
 
7.8%
t238336
 
5.5%
n230993
 
5.3%
a215501
 
4.9%
r210639
 
4.8%
o191610
 
4.4%
y137977
 
3.2%
Other values (35)1448909
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)4372956
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
481996
 
11.0%
s439542
 
10.1%
e437496
 
10.0%
i339957
 
7.8%
t238336
 
5.5%
n230993
 
5.3%
a215501
 
4.9%
r210639
 
4.8%
o191610
 
4.4%
y137977
 
3.2%
Other values (35)1448909
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4372956
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
481996
 
11.0%
s439542
 
10.1%
e437496
 
10.0%
i339957
 
7.8%
t238336
 
5.5%
n230993
 
5.3%
a215501
 
4.9%
r210639
 
4.8%
o191610
 
4.4%
y137977
 
3.2%
Other values (35)1448909
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4372956
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
481996
 
11.0%
s439542
 
10.1%
e437496
 
10.0%
i339957
 
7.8%
t238336
 
5.5%
n230993
 
5.3%
a215501
 
4.9%
r210639
 
4.8%
o191610
 
4.4%
y137977
 
3.2%
Other values (35)1448909
33.1%

diag_3_category
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
Diseases Of The Circulatory System
29918 
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
26308 
Diseases Of The Respiratory System
6774 
Diseases Of The Genitourinary System
6327 
Symptoms, Signs, And Ill-Defined Conditions
4523 
Other values (14)
27916 

Length

Max length98
Median length71
Mean length45.9915
Min length7

Characters and Unicode

Total characters4680371
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
3rd rowSupplementary Classification Of Factors Influencing Health Status And Contact With Health Services
4th rowDiseases Of The Circulatory System
5th rowEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders

Common Values

ValueCountFrequency (%)
Diseases Of The Circulatory System29918
29.4%
Endocrine, Nutritional And Metabolic Diseases, And Immunity Disorders26308
25.9%
Diseases Of The Respiratory System6774
 
6.7%
Diseases Of The Genitourinary System6327
 
6.2%
Symptoms, Signs, And Ill-Defined Conditions4523
 
4.4%
Supplementary Classification Of Factors Influencing Health Status And Contact With Health Services3814
 
3.7%
Diseases Of The Digestive System3572
 
3.5%
Mental Disorders3136
 
3.1%
Diseases Of The Blood And Blood-Forming Organs2490
 
2.4%
Diseases Of The Skin And Subcutaneous Tissue2488
 
2.4%
Other values (9)12416
12.2%

Length

2026-01-02T11:43:01.097911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diseases83419
13.6%
and74972
12.2%
of61861
10.1%
the55559
 
9.1%
system50272
 
8.2%
circulatory29918
 
4.9%
disorders29444
 
4.8%
endocrine26308
 
4.3%
nutritional26308
 
4.3%
metabolic26308
 
4.3%
Other values (42)148111
24.2%

Most occurring characters

ValueCountFrequency (%)
510714
 
10.9%
e443515
 
9.5%
s441319
 
9.4%
i370597
 
7.9%
n274935
 
5.9%
t266903
 
5.7%
a233969
 
5.0%
r226271
 
4.8%
o210506
 
4.5%
d145059
 
3.1%
Other values (35)1556583
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4680371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
510714
 
10.9%
e443515
 
9.5%
s441319
 
9.4%
i370597
 
7.9%
n274935
 
5.9%
t266903
 
5.7%
a233969
 
5.0%
r226271
 
4.8%
o210506
 
4.5%
d145059
 
3.1%
Other values (35)1556583
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4680371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
510714
 
10.9%
e443515
 
9.5%
s441319
 
9.4%
i370597
 
7.9%
n274935
 
5.9%
t266903
 
5.7%
a233969
 
5.0%
r226271
 
4.8%
o210506
 
4.5%
d145059
 
3.1%
Other values (35)1556583
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4680371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
510714
 
10.9%
e443515
 
9.5%
s441319
 
9.4%
i370597
 
7.9%
n274935
 
5.9%
t266903
 
5.7%
a233969
 
5.0%
r226271
 
4.8%
o210506
 
4.5%
d145059
 
3.1%
Other values (35)1556583
33.3%

Interactions

2026-01-02T11:42:57.969103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:54.668750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:54.982502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.285629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.588784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.908477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.220607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.529548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.862224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.364761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.674869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.998927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-02T11:42:55.937825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-02T11:42:56.417676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.746335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-02T11:42:58.200404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-01-02T11:42:55.824665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.135012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.444605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.774568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.250296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.589840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.889310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:58.229533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:54.926572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.231163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.532100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.853709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.163453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.473082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.804411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.276742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.618809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.916759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:58.257568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:54.953968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.257987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.557472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:55.880022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.190868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.499842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:56.832697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.334360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.644947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-02T11:42:57.941490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-02T11:43:01.135352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A1Cresultacarboseacetohexamideadmission_source_idadmission_type_idagechangechlorpropamidediabetesMeddiag_1_categorydiag_2_categorydiag_3_categorydischarge_disposition_idgenderglimepirideglimepiride-pioglitazoneglipizideglipizide-metforminglyburideglyburide-metformininsulinmax_glu_serummetforminmetformin-pioglitazonemetformin-rosiglitazonemiglitolnateglinidenum_lab_proceduresnum_medicationsnum_proceduresnumber_diagnosesnumber_emergencynumber_inpatientnumber_outpatientpioglitazoneracereadmittedrepagliniderosiglitazonetime_in_hospitaltolazamidetolbutamidetroglitazoneweight
A1Cresult1.0000.0120.0000.0640.0620.1060.1150.0000.0960.0990.0730.0570.0300.0160.0200.0000.0230.0060.0170.0000.0800.0430.0420.0000.0040.0000.0010.1490.0180.0380.0530.0000.0220.0090.0110.0300.0180.0200.0100.0390.0100.0000.0000.029
acarbose0.0121.0000.0000.0000.0000.0020.0460.0000.0300.0070.0000.0000.0000.0070.0100.0000.0220.0000.0070.0040.0110.0020.0130.0000.0000.0010.0000.0000.0130.0040.0000.0000.0070.0000.0070.0080.0120.0120.0020.0070.0000.0000.0000.006
acetohexamide0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0190.0130.0000.0000.0000.0000.0000.0000.0000.0000.0000.0190.0000.0000.0000.000
admission_source_id0.0640.0000.0001.000-0.3830.0350.0230.0000.0180.1310.0570.0400.0420.0120.0160.0000.0070.0000.0190.0170.0410.3630.0290.0000.0000.0000.0070.136-0.063-0.2050.1060.1040.0560.0240.0170.0480.0560.0180.0210.0030.0000.0040.0000.078
admission_type_id0.0620.0000.000-0.3831.0000.0380.0630.0020.0430.1390.0650.0480.0210.0130.0360.0000.0120.0000.0070.0270.0640.3410.0320.0000.0000.0050.012-0.2240.0870.217-0.127-0.033-0.0450.0300.0200.0660.0440.0340.019-0.0150.0060.0130.0000.097
age0.1060.0020.0000.0350.0381.0000.0560.0030.0440.1620.1530.1460.0600.0780.0240.0000.0370.0000.0500.0100.0680.0360.0660.0000.0000.0040.0080.0230.0600.0650.1310.0270.0490.0040.0300.0950.0380.0290.0260.0430.0000.0140.0000.022
change0.1150.0460.0000.0230.0630.0561.0000.0120.5060.1180.0710.0470.0810.0140.1440.0000.2090.0070.1910.0430.6410.0570.3290.0000.0000.0140.0550.0700.2440.0270.0570.0150.0170.0150.2030.0200.0460.0780.1960.1150.0000.0000.0030.046
chlorpropamide0.0000.0000.0000.0000.0020.0030.0121.0000.0150.0000.0000.0000.0190.0000.0000.0000.0020.0000.0000.0000.0100.0000.0030.0000.0000.0000.0000.0000.0000.0030.0060.0000.0000.0000.0000.0040.0040.0000.0000.0030.0000.0000.0000.000
diabetesMed0.0960.0300.0000.0180.0430.0440.5060.0151.0000.1000.0600.0420.0830.0150.1270.0000.2060.0040.1870.0450.5850.0500.2700.0000.0000.0090.0450.0430.1960.0300.0320.0070.0180.0010.1520.0110.0610.0680.1410.0700.0100.0070.0000.035
diag_1_category0.0990.0070.0000.1310.1390.1620.1180.0000.1001.0000.2540.2030.0840.0730.0250.0000.0480.0030.0460.0140.1030.0510.0790.0040.0000.0030.0140.0740.0980.1850.0720.0140.0310.0180.0350.0570.0630.0150.0310.0980.0000.0000.0000.062
diag_2_category0.0730.0000.0000.0570.0650.1530.0710.0000.0600.2541.0000.1930.0600.0650.0190.0180.0260.0110.0270.0070.0590.0350.0550.0000.0000.0140.0070.0470.0700.1070.1430.0180.0230.0180.0190.0470.0530.0150.0200.0590.0000.0070.0140.104
diag_3_category0.0570.0000.0000.0400.0480.1460.0470.0000.0420.2030.1931.0000.0540.0510.0150.0060.0230.0000.0260.0030.0440.0270.0460.0000.0000.0070.0000.0470.0680.0720.3230.0160.0220.0160.0200.0330.0520.0130.0130.0630.0000.0070.0000.073
discharge_disposition_id0.0300.0000.0200.0420.0210.0600.0810.0190.0830.0840.0600.0541.0000.0270.0220.0000.0280.0100.0510.0150.0780.0740.0360.0000.0000.0040.0060.0590.1710.0130.1510.0070.0850.0330.0240.0270.1200.0160.0170.2760.0170.0100.0110.050
gender0.0160.0070.0000.0120.0130.0780.0140.0000.0150.0730.0650.0510.0271.0000.0000.0000.0190.0050.0230.0000.0000.0000.0000.0000.0020.0040.0000.0170.0360.0450.0000.0000.0080.0000.0040.0510.0130.0000.0110.0280.0030.0000.0040.007
glimepiride0.0200.0100.0000.0160.0360.0240.1440.0000.1270.0250.0190.0150.0220.0001.0000.0000.0420.0000.0400.0040.0100.0190.0280.0000.0000.0130.0090.0190.0290.0070.0100.0090.0000.0000.0260.0130.0070.0030.0250.0250.0000.0000.0050.007
glimepiride-pioglitazone0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0060.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
glipizide0.0230.0220.0000.0070.0120.0370.2090.0020.2060.0480.0260.0230.0280.0190.0420.0001.0000.0000.0620.0150.0340.0110.0490.0000.0000.0140.0070.0240.0420.0090.0130.0000.0120.0000.0290.0140.0150.0100.0270.0370.0000.0020.0000.022
glipizide-metformin0.0060.0000.0000.0000.0000.0000.0070.0000.0040.0030.0110.0000.0100.0050.0000.0000.0001.0000.0000.0300.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0050.0000.0000.0000.000
glyburide0.0170.0070.0000.0190.0070.0500.1910.0000.1870.0460.0270.0260.0510.0230.0400.0000.0620.0001.0000.0040.0540.0060.0930.0000.0000.0000.0110.0200.0300.0070.0240.0000.0200.0050.0160.0170.0040.0140.0250.0330.0000.0000.0000.011
glyburide-metformin0.0000.0040.0000.0170.0270.0100.0430.0000.0450.0140.0070.0030.0150.0000.0040.0000.0150.0300.0041.0000.0050.0090.0120.0000.0000.0000.0040.0060.0030.0000.0120.0200.0000.0000.0180.0090.0040.0030.0020.0030.0000.0000.0000.014
insulin0.0800.0110.0000.0410.0640.0680.6410.0100.5850.1030.0590.0440.0780.0000.0100.0000.0340.0000.0540.0051.0000.0470.0320.0000.0030.0040.0040.0730.1430.0230.0780.0170.0440.0180.0090.0350.0500.0180.0130.0790.0080.0000.0000.095
max_glu_serum0.0430.0020.0000.3630.3410.0360.0570.0000.0500.0510.0350.0270.0740.0000.0190.0000.0110.0000.0060.0090.0471.0000.0190.0000.0000.0000.0090.2430.0290.0450.0550.0000.0280.0130.0110.0380.0150.0100.0030.0290.0000.0130.0000.042
metformin0.0420.0130.0000.0290.0320.0660.3290.0030.2700.0790.0550.0460.0360.0000.0280.0000.0490.0000.0930.0120.0320.0191.0000.0410.0000.0080.0130.0370.0440.0230.0460.0000.0320.0070.0340.0120.0220.0090.0610.0280.0080.0050.0000.011
metformin-pioglitazone0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0411.0000.0000.0000.0000.0010.0000.0000.0050.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.000
metformin-rosiglitazone0.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0000.0000.0030.0000.0000.0001.0000.0000.0000.0000.0380.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
miglitol0.0000.0010.0000.0000.0050.0040.0140.0000.0090.0030.0140.0070.0040.0040.0130.0000.0140.0000.0000.0000.0040.0000.0080.0000.0001.0000.0050.0000.0000.0000.0000.0000.0030.0000.0000.0000.0050.0080.0000.0090.0000.0000.0000.000
nateglinide0.0010.0000.0000.0070.0120.0080.0550.0000.0450.0140.0070.0000.0060.0000.0090.0000.0070.0000.0110.0040.0040.0090.0130.0000.0000.0051.0000.0060.0150.0010.0350.0210.0000.0000.0200.0100.0000.0000.0090.0050.0000.0000.0000.010
num_lab_procedures0.1490.0000.0040.136-0.2240.0230.0700.0000.0430.0740.0470.0470.0590.0170.0190.0000.0240.0080.0200.0060.0730.2430.0370.0010.0000.0000.0061.0000.2520.0230.1690.0060.041-0.0240.0180.0450.0320.0200.0110.3370.0000.0060.0000.112
num_medications0.0180.0130.019-0.0630.0870.0600.2440.0000.1960.0980.0700.0680.1710.0360.0290.0000.0420.0000.0300.0030.1430.0290.0440.0000.0380.0000.0150.2521.0000.3520.2940.0440.0990.0740.0430.0340.0630.0160.0320.4650.0000.0000.0000.028
num_procedures0.0380.0040.013-0.2050.2170.0650.0270.0030.0300.1850.1070.0720.0130.0450.0070.0000.0090.0000.0070.0000.0230.0450.0230.0000.0060.0000.0010.0230.3521.0000.067-0.046-0.064-0.0240.0100.0260.0370.0000.0080.1870.0070.0000.0000.019
number_diagnoses0.0530.0000.0000.106-0.1270.1310.0570.0060.0320.0720.1430.3230.1510.0000.0100.0050.0130.0000.0240.0120.0780.0550.0460.0050.0000.0000.0350.1690.2940.0671.0000.0920.1360.1130.0100.0480.0820.0220.0080.2370.0090.0000.0000.063
number_emergency0.0000.0000.0000.104-0.0330.0270.0150.0000.0070.0140.0180.0160.0070.0000.0090.0000.0000.0000.0000.0200.0170.0000.0000.0000.0000.0000.0210.0060.044-0.0460.0921.0000.2220.1770.0000.0060.0290.0000.000-0.0010.0000.0000.0000.000
number_inpatient0.0220.0070.0000.056-0.0450.0490.0170.0000.0180.0310.0230.0220.0850.0080.0000.0000.0120.0000.0200.0000.0440.0280.0320.0000.0000.0030.0000.0410.099-0.0640.1360.2221.0000.1560.0110.0100.1300.0000.0080.0920.0000.0000.0000.026
number_outpatient0.0090.0000.0000.0240.0300.0040.0150.0000.0010.0180.0180.0160.0330.0000.0000.0000.0000.0000.0050.0000.0180.0130.0070.0000.0000.0000.000-0.0240.074-0.0240.1130.1770.1561.0000.0000.0130.0280.0000.000-0.0130.0000.0000.0000.058
pioglitazone0.0110.0070.0000.0170.0200.0300.2030.0000.1520.0350.0190.0200.0240.0040.0260.0000.0290.0000.0160.0180.0090.0110.0340.0100.0000.0000.0200.0180.0430.0100.0100.0000.0110.0001.0000.0150.0110.0150.0370.0230.0000.0000.0000.018
race0.0300.0080.0000.0480.0660.0950.0200.0040.0110.0570.0470.0330.0270.0510.0130.0000.0140.0000.0170.0090.0350.0380.0120.0000.0000.0000.0100.0450.0340.0260.0480.0060.0100.0130.0151.0000.0190.0140.0070.0150.0000.0020.0000.079
readmitted0.0180.0120.0000.0560.0440.0380.0460.0040.0610.0630.0530.0520.1200.0130.0070.0000.0150.0010.0040.0040.0500.0150.0220.0000.0000.0050.0000.0320.0630.0370.0820.0290.1300.0280.0110.0191.0000.0160.0130.0480.0020.0000.0000.048
repaglinide0.0200.0120.0000.0180.0340.0290.0780.0000.0680.0150.0150.0130.0160.0000.0030.0000.0100.0000.0140.0030.0180.0100.0090.0000.0000.0080.0000.0200.0160.0000.0220.0000.0000.0000.0150.0140.0161.0000.0060.0240.0000.0000.0000.002
rosiglitazone0.0100.0020.0000.0210.0190.0260.1960.0000.1410.0310.0200.0130.0170.0110.0250.0000.0270.0000.0250.0020.0130.0030.0610.0000.0000.0000.0090.0110.0320.0080.0080.0000.0080.0000.0370.0070.0130.0061.0000.0210.0000.0000.0030.000
time_in_hospital0.0390.0070.0190.003-0.0150.0430.1150.0030.0700.0980.0590.0630.2760.0280.0250.0000.0370.0050.0330.0030.0790.0290.0280.0000.0000.0090.0050.3370.4650.1870.237-0.0010.092-0.0130.0230.0150.0480.0240.0211.0000.0000.0000.0130.028
tolazamide0.0100.0000.0000.0000.0060.0000.0000.0000.0100.0000.0000.0000.0170.0030.0000.0000.0000.0000.0000.0000.0080.0000.0080.0000.0000.0000.0000.0000.0000.0070.0090.0000.0000.0000.0000.0000.0020.0000.0000.0001.0000.0000.0000.000
tolbutamide0.0000.0000.0000.0040.0130.0140.0000.0000.0070.0000.0070.0070.0100.0000.0000.0000.0020.0000.0000.0000.0000.0130.0050.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0020.0000.0000.0000.0000.0001.0000.0000.000
troglitazone0.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0140.0000.0110.0040.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0130.0000.0001.0000.000
weight0.0290.0060.0000.0780.0970.0220.0460.0000.0350.0620.1040.0730.0500.0070.0070.0000.0220.0000.0110.0140.0950.0420.0110.0000.0000.0000.0100.1120.0280.0190.0630.0000.0260.0580.0180.0790.0480.0020.0000.0280.0000.0000.0001.000

Missing values

2026-01-02T11:42:58.365822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-02T11:42:58.616476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

racegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalmedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitteddiag_1_categorydiag_2_categorydiag_3_category
0CaucasianFemale[0-10)062511Pediatrics-Endocrinology41010001NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNOEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersUnknownUnknown
1CaucasianFemale[10-20)01173Unknown590180009NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYes>30Endocrine, Nutritional And Metabolic Diseases, And Immunity DisordersEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
2AfricanAmericanFemale[20-30)01172Unknown115132016NotPerformedNotPerformedNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoYesNOComplications Of Pregnancy, Childbirth, And The PuerperiumEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersSupplementary Classification Of Factors Influencing Health Status And Contact With Health Services
3CaucasianMale[30-40)01172Unknown441160007NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNOInfectious And Parasitic DiseasesEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersDiseases Of The Circulatory System
4CaucasianMale[40-50)01171Unknown51080005NotPerformedNotPerformedNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNONeoplasmsNeoplasmsEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
5CaucasianMale[50-60)02123Unknown316160009NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30Diseases Of The Circulatory SystemDiseases Of The Circulatory SystemEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
6CaucasianMale[60-70)03124Unknown701210007NotPerformedNotPerformedSteadyNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNODiseases Of The Circulatory SystemDiseases Of The Circulatory SystemSupplementary Classification Of Factors Influencing Health Status And Contact With Health Services
7CaucasianMale[70-80)01175Unknown730120008NotPerformedNotPerformedNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoYes>30Diseases Of The Circulatory SystemDiseases Of The Respiratory SystemEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
8CaucasianFemale[80-90)021413Unknown682280008NotPerformedNotPerformedNoNoNoNoNoNoSteadyNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoChYesNODiseases Of The Circulatory SystemDiseases Of The Circulatory SystemInfectious And Parasitic Diseases
9CaucasianFemale[90-100)033412InternalMedicine333180008NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoSteadyNoNoNoNoNoChYesNODiseases Of The Circulatory SystemNeoplasmsDiseases Of The Respiratory System
racegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalmedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitteddiag_1_categorydiag_2_categorydiag_3_category
101756OtherFemale[60-70)01172Unknown466171119NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYes>30Injury And PoisoningDiseases Of The Genitourinary SystemDiseases Of The Circulatory System
101757CaucasianFemale[70-80)01175Unknown211160019NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNODiseases Of The Respiratory SystemDiseases Of The Respiratory SystemDiseases Of The Respiratory System
101758CaucasianFemale[80-90)01175Unknown761220109NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNOMental DisordersInfectious And Parasitic DiseasesMental Disorders
101759CaucasianMale[80-90)01171Unknown10153007NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoUpNoNoNoNoNoChYesNODiseases Of The Circulatory SystemSymptoms, Signs, And Ill-Defined ConditionsEndocrine, Nutritional And Metabolic Diseases, And Immunity Disorders
101760AfricanAmericanFemale[60-70)01176Unknown451253129NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoDownNoNoNoNoNoChYes>30Diseases Of The Nervous System And Sense OrgansDiseases Of The Circulatory SystemDiseases Of The Circulatory System
101761AfricanAmericanMale[70-80)01373Unknown510160009NotPerformed>8SteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYes>30Endocrine, Nutritional And Metabolic Diseases, And Immunity DisordersMental DisordersDiseases Of The Circulatory System
101762AfricanAmericanFemale[80-90)01455Unknown333180019NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNODiseases Of The Digestive SystemEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersSymptoms, Signs, And Ill-Defined Conditions
101763CaucasianMale[70-80)01171Unknown530910013NotPerformedNotPerformedSteadyNoNoNoNoNoNoNoNoNoNoNoNoNoNoDownNoNoNoNoNoChYesNOInfectious And Parasitic DiseasesDiseases Of The Genitourinary SystemMental Disorders
101764CaucasianFemale[80-90)023710Surgery-General452210019NotPerformedNotPerformedNoNoNoNoNoNoSteadyNoNoSteadyNoNoNoNoNoUpNoNoNoNoNoChYesNOInjury And PoisoningDiseases Of The Blood And Blood-Forming OrgansInjury And Poisoning
101765CaucasianMale[70-80)01176Unknown13330009NotPerformedNotPerformedNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNoNODiseases Of The Digestive SystemDiseases Of The Digestive SystemSymptoms, Signs, And Ill-Defined Conditions

Duplicate rows

Most frequently occurring

racegenderageweightadmission_type_iddischarge_disposition_idadmission_source_idtime_in_hospitalmedical_specialtynum_lab_proceduresnum_proceduresnum_medicationsnumber_outpatientnumber_emergencynumber_inpatientnumber_diagnosesmax_glu_serumA1Cresultmetforminrepaglinidenateglinidechlorpropamideglimepirideacetohexamideglipizideglyburidetolbutamidepioglitazonerosiglitazoneacarbosemiglitoltroglitazonetolazamideinsulinglyburide-metforminglipizide-metforminglimepiride-pioglitazonemetformin-rosiglitazonemetformin-pioglitazonechangediabetesMedreadmitteddiag_1_categorydiag_2_categorydiag_3_category# duplicates
0AfricanAmericanFemale[10-20)01173Pediatrics-Endocrinology51030001NotPerformed>8NoNoNoNoNoNoNoNoNoNoNoNoNoNoNoSteadyNoNoNoNoNoNoYesNOEndocrine, Nutritional And Metabolic Diseases, And Immunity DisordersUnknownUnknown2